75,218 research outputs found
Neighbor cache prefetching for multimedia image and video processing
Cache performance is strongly influenced by the type of locality embodied in programs. In particular, multimedia programs handling images and videos are characterized by a bidimensional spatial locality, which is not adequately exploited by standard caches. In this paper we propose novel cache prefetching techniques for image data, called neighbor prefetching, able to improve exploitation of bidimensional spatial locality. A performance comparison is provided against other assessed prefetching techniques on a multimedia workload (with MPEG-2 and MPEG-4 decoding, image processing, and visual object segmentation), including a detailed evaluation of both the miss rate and the memory access time. Results prove that neighbor prefetching achieves a significant reduction in the time due to delayed memory cycles (more than 97% on MPEG-4 with respect to 75% of the second performing technique). This reduction leads to a substantial speedup on the overall memory access time (up to 140% for MPEG-4). Performance has been measured with the PRIMA trace-driven simulator, specifically devised to support cache prefetching
Understanding Citizen Reactions and Ebola-Related Information Propagation on Social Media
In severe outbreaks such as Ebola, bird flu and SARS, people share news, and
their thoughts and responses regarding the outbreaks on social media.
Understanding how people perceive the severe outbreaks, what their responses
are, and what factors affect these responses become important. In this paper,
we conduct a comprehensive study of understanding and mining the spread of
Ebola-related information on social media. In particular, we (i) conduct a
large-scale data-driven analysis of geotagged social media messages to
understand citizen reactions regarding Ebola; (ii) build information
propagation models which measure locality of information; and (iii) analyze
spatial, temporal and social properties of Ebola-related information. Our work
provides new insights into Ebola outbreak by understanding citizen reactions
and topic-based information propagation, as well as providing a foundation for
analysis and response of future public health crises.Comment: 2016 IEEE/ACM International Conference on Advances in Social Networks
Analysis and Mining (ASONAM 2016
Resource contrast in patterned peatlands increases along a climatic gradient
Copyright by the Ecological Society of America 2010, for personal or educational use only. Article is available at <http://dx.doi.org/10.1890/09-1313.1
Domain Generalization Strategy to Train Classifiers Robust to Spatial-Temporal Shift
Deep learning-based weather prediction models have advanced significantly in
recent years. However, data-driven models based on deep learning are difficult
to apply to real-world applications because they are vulnerable to
spatial-temporal shifts. A weather prediction task is especially susceptible to
spatial-temporal shifts when the model is overfitted to locality and
seasonality. In this paper, we propose a training strategy to make the weather
prediction model robust to spatial-temporal shifts. We first analyze the effect
of hyperparameters and augmentations of the existing training strategy on the
spatial-temporal shift robustness of the model. Next, we propose an optimal
combination of hyperparameters and augmentation based on the analysis results
and a test-time augmentation. We performed all experiments on the W4C22
Transfer dataset and achieved the 1st performance.Comment: Core Transfer Track 1st place solution in Weather4Cast competition at
NeuIPS2
Statistical Traffic State Analysis in Large-scale Transportation Networks Using Locality-Preserving Non-negative Matrix Factorization
Statistical traffic data analysis is a hot topic in traffic management and
control. In this field, current research progresses focus on analyzing traffic
flows of individual links or local regions in a transportation network. Less
attention are paid to the global view of traffic states over the entire
network, which is important for modeling large-scale traffic scenes. Our aim is
precisely to propose a new methodology for extracting spatio-temporal traffic
patterns, ultimately for modeling large-scale traffic dynamics, and long-term
traffic forecasting. We attack this issue by utilizing Locality-Preserving
Non-negative Matrix Factorization (LPNMF) to derive low-dimensional
representation of network-level traffic states. Clustering is performed on the
compact LPNMF projections to unveil typical spatial patterns and temporal
dynamics of network-level traffic states. We have tested the proposed method on
simulated traffic data generated for a large-scale road network, and reported
experimental results validate the ability of our approach for extracting
meaningful large-scale space-time traffic patterns. Furthermore, the derived
clustering results provide an intuitive understanding of spatial-temporal
characteristics of traffic flows in the large-scale network, and a basis for
potential long-term forecasting.Comment: IET Intelligent Transport Systems (2013
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